Biology subjects

De Luca, A.

Publications and source records attributed to De Luca, A..

13 recordsLinked to original sources

bioRxiv2026

The D2-mdx mouse as a preclinical model for Duchenne muscular dystrophy: a natural history study across two independent sites

IntroductionThe quality of preclinical studies for rare diseases, such as Duchenne muscular dystrophy (DMD), relies on the availability of comprehensive natural disease history data. In addition to the classic BL10-mdx mouse, in recent years, the D2-mdx model has increasingly been used as an alternative model due to its reportedly more severely impaired phenotype. To improve our understanding of disease progression in these two DMD models, we conducted a comprehensive natural history study. Materials and MethodsThis involved a cross-sectional analysis of key in vivo and ex vivo outcome measures performed in two independent laboratories, using the same study setup in compliance with TREAT-NMD Standard Operating Procedures (SOPs), while also taking advantage of site-specific expertise. Globally, largely comparable results were obtained across the two study sites. ResultsBody composition showed pronounced differences between the strains, with BL10-mdx mice displaying a hypertrophic and D2-mdx mice displaying an atrophic phenotype. Dystrophic mice of each strain exhibited significant alterations of disease-relevant indices related to muscle functionality and integrity, mostly worsening with age, in comparison to their wildtypes. Cardiac function was affected earlier and more severely in D2-mdx mice. DiscussionNotably, for some parameters, genetic-background related differences were observed, emphasizing the need to include control groups with matching genetic backgrounds in experimental designs. ConclusionsCollectively, our natural history study provides benchmark data for these two mdx mouse strains to guide model selection for preclinical DMD studies, allowing accurate data interpretation. HighlightsO_LIDistinct body composition phenotypes: BL10-mdx mice exhibit pseudohypertrophy while D2-mdx mice display pronounced atrophy. C_LIO_LIEarlier cardiac dysfunction in D2-mdx: D2-mdx mice develop reduced ejection fraction and stroke volume from 28 weeks, while BL10-mdx only at 52 weeks. C_LIO_LIGenetic background-dependent variations: Intrinsic deficits in wildtype D2 mice demonstrate that genetic background influences outcome measures independent of dystrophic pathology. C_LIO_LIComparable ex vivo muscle physiology: Despite divergent in vivo phenotypes, isolated muscle contractile parameters show similar impairment in both dystrophic models. C_LIO_LIMulti-site standardized validation: Cross-sectional study at two independent laboratories following harmonized TREAT-NMD Standard Operating Procedures. C_LI

pharmacology and toxicology↗

bioRxiv2026

A CYFIP1-Inspired Peptidomimetic Modulates eIF4E-Dependent Translational Control in Cancer and Neurodevelopmental Disorders

The eukaryotic translation initiation factor 4E (eIF4E) is a central regulator of cap-dependent translation and a compelling pharmacological target in disorders marked by protein synthesis dysregulation, including cancer and Fragile X Syndrome (FXS). Among endogenous eIF4E regulators, the CYFIP1-eIF4E interaction is uniquely selective, offering a framework for designing targeted translation modulators. Here, we report Cy-9B, a rationally engineered, stapled peptidomimetic derived from CYFIP1 that binds eIF4E, disrupts eIF4E-eIF4G complex, and suppresses cap-dependent translation. Enhanced-sampling free-energy simulations reveal that Cy-9B engages eIF4E through a non-canonical binding mode. Cy-9B exhibits drug-like properties, including high proteolytic stability and nanomolar affinity. Functionally, Cy-9B inhibits lung cancer cell proliferation, migration, and invasion. In neurodevelopmental disease models, Cy-9B partially normalizes excessive translation in FXS hippocampal neurons and rescues social behavior deficits in a Cyfip1 haploinsufficient Drosophila melanogaster model, restoring wild-type-like performance. Cy-9B emerges as a first-in-class therapeutic candidate for disorders sharing translational dysregulation, highlighting targeted modulation of eIF4E as a broadly applicable and physiologically compatible therapeutic strategy.

biochemistry↗

bioRxiv2024

Cross-site harmonization of diffusion MRI data without matched training subjects

PurposeDiffusion MRI (dMRI) data typically suffer of significant cross-site variability, which prevents naively performing pooled analyses. To attenuate cross-site variability, harmonization methods such as the rotational invariant spherical harmonics (RISH) have been introduced to harmonize the dMRI data at the signal level. A common requirement of the RISH method, is the availability of healthy individuals who are matched at the group level, which may not always be readily available, particularly retrospectively. In this work, we propose a framework to harmonize dMRI without matched training groups. MethodsOur framework learns harmonization features while controlling for potential covariates using a voxel-based generalized linear model (RISH-GLM). RISH-GLM allows to simultaneously harmonize data from any number of sites while also accounting for covariates of interest, thus not requiring matched training subjects. Additionally, RISH-GLM can harmonize data from multiple sites in a single step, whereas RISH is performed for each site independently. ResultsWe considered data of training subjects from retrospective cohorts acquired with 3 different scanners and performed 3 harmonization experiments of increasing complexity. First, we demonstrate that RISH-GLM is equivalent to conventional RISH when trained with data of matched training subjects. Secondly, we demonstrate that RISH-GLM can effectively learn harmonization with two groups of highly unmatched subjects. Thirdly, we evaluate the ability of RISH-GLM to simultaneously harmonize data from 3 different sites. DiscussionRISH-GLM can learn cross-site harmonization both from matched and unmatched groups of training subjects, and can effectively be used to harmonize data of multiple sites in one single step.

bioinformatics↗

bioRxiv2022

Adipocyte differentiation of 3T3-L1 cells under TAF, TDF and INSTIs selective challenge: an in vitro model.

Integrase strand transfer inhibitors (INSTI) are a recently available class of antiretroviral therapy (ART) medications with a good tolerability profile and a high genetic barrier to HIV drug resistance. However, several studies report more significant weight gain among persons receiving INSTI-based ART regimens for initial therapy compared to protease inhibitors (PIs) and nucleoside reverse transcriptase inhibitors (NNRTI)-based regimens. In our experimental setting, we used the in vitro model of adipogenesis of 3T3-L1 cells to investigate the effects of the NRTIs tenofovir disoproxil fumarate (TDF) and tenofovir alafenamide (TAF), alone or in combination with four integrase strand transfer inhibitors: raltegravir (RAL), elvitegravir (ELV), dolutegravir (DTG) and bictegravir (BIC) on adipose differentiation. In addition, protein expression levels of PPAR{gamma} and C/EBP, and the intracellular lipid accumulation by Red Oil staining, were used to monitor adipocyte differentiation. Compared to control, RAL, ELV, DTG, and BIC were all able to increase adipogenesis, being in this, RAL and ELV more efficient. On the other hand, TAF and TDF inhibited adipogenesis. Moreover, when used in combination with the other INSTI molecules, TAF and TDF were able to reduce the adipogenic effects of all four drugs. This ability was more evident when TAF was used in combination with DTG and BIC. All these data suggest that TAF and TDF have an inhibitory effect on adipogenesis in vitro and that they could also effectively counteract the increased adipogenesis caused by the treatment with INSTIs. Finally, to evaluate if the 3T3-L1 cell could express fibroblast-like features following INSTIs treatment, we evaluated the immunohistochemical expression of ER-TR7, a well-known fibroblastic marker. This last assay showed that treatment with INSTIs increased the expression of ER-TR7 compared to control and to cells treated with TAF o TDF. In conclusion, our experimental data support the evidence that in vitro challenge of 3T3-L1 cells with INSTIs is able to increase adipocytic differentiation and to drive a number of these cells toward the expression of fibroblastic features, with a different degree according to the various drugs used, while TAF and TDF have an antagonistic role on this phenomenon.

pharmacology and toxicology↗

bioRxiv2022

LAMC2 marks a tumor-initiating cell population with an aggressive signature in pancreatic cancer

Tumor-initiating cells (TIC), also known as cancer stem cells, are considered a specific subpopulation of cells necessary for cancer initiation and metastasis. Here, we report a LAMC2-positive cell population, which is endowed with enhanced self-renewal capacity, and is sufficient for tumor initiation, differentiation, and driving metastasis. Mechanistically, mRNA profiling of these cells indicate a prominent squamous signature, and differentially activated pathways critical for tumor growth and metastasis, including deregulation of the TGF-{beta} signaling pathway. Treatment with Vactosertib, a new small molecule inhibitor of transforming growth factor-{beta} (TGF-{beta}) type I receptor (activin receptor-like kinase-5, ALK5), completely abrogated the lung metastasis, primarily originating from LAMC2 expressing cells. Our results prompt further study of this TIC population in pancreatic cancer and exploration as a potential therapeutic target and/or biomarker.

cancer biology↗

bioRxiv2021

Insights from the IronTract challenge: optimal methods for mapping brain pathways from multi-shell diffusion MRI

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.

neuroscience↗

bioRxiv2021

Sodium valproate increases activity of the sirtuin pathway resulting in beneficialeffects for spinocerebellar ataxia-3 in vivo

Machado-Joseph disease (MJD, also known as spinocerebellar ataxia-3) is a fatal neurodegenerative disease that impairs control and coordination of movement. Here we tested whether treatment with the histone deacetylase inhibitor sodium valproate (SV) prevented a movement phenotype that develops in larvae of a transgenic zebrafish model of the disease. We found that treatment with SV improved the swimming of the MJD zebrafish, increased levels of acetylated histones 3 and 4, but also increased expression of polyglutamine expanded human ataxin-3. Proteomic analysis of protein lysates generated from the treated and untreated MJD zebrafish also predicted that SV treatment had activated the sirtuin longevity signaling pathway and this was confirmed by findings of increased SIRT1 protein levels and sirtuin activity in SV treated MJD zebrafish and HEK293 cells expressing ataxin-3-84Q, respectively. Treatment with resveratrol (another compound known to activate the sirtuin pathway), also improved swimming in the MJD zebrafish. Co-treatment with SV alongside EX527, a SIRT1 activity inhibitor, prevented induction of autophagy by SV and the beneficial effects of SV on the movement in the MJD zebrafish, indicating that they were both dependent on sirtuin activity. These findings provide the first evidence of sodium valproate inducing activation of the sirtuin pathway. Further, they indicate that drugs that target the sirtuin pathway, including sodium valproate and resveratrol, warrant further investigation for the treatment of MJD and related neurodegenerative diseases.

neuroscience↗

bioRxiv2021

On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge.

Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. Most predictions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.

neuroscience↗

bioRxiv2020

Anatomically informed multi-level fiber tractography

Diffusion weighted MR imaging can assist preoperative planning by reconstructing the trajectory of eloquent fiber pathways. A common task is the delineation of the corticospinal tract in its full extent because lesions to this bundle can severely affect the quality of life. However, this is challenging as existing tractography algorithms typically produce either incomplete results or multiple false-positive tracts. In this work, we suggest a novel approach to fiber tractography that reconstructs multi-level structures by progressively taking into account previously unused fiber orientations. Anatomical priors are used in order to minimize the number of false-positive pathways. The devised method was evaluated on synthetic data with different noise levels. Additionally, it was tested on in-vivo data by reconstructing the corticospinal tract and it was compared to conventional deterministic and probabilistic approaches. The corticospinal tract reconstructed by our method includes lateral projections that could not be observed with deterministic methods, while avoiding spurious tracts reconstructed by probabilistic tractography. Furthermore, the proposed algorithm preserves the neuroanatomical topology of the pathways to a larger extent as compared to probabilistic tractography.

neuroscience↗

bioRxiv2020

BioID analysis of the cyclin F interactome reveals that ALS-variant cyclin F alters the homeostasis of paraspeckle-associated proteins

BackgroundPreviously, we identified missense mutations in CCNF that are causative of familial and sporadic amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). CCNF encodes for the protein cyclin F, a substrate recognition component of the E3-ubiquitin ligase, SCFcyclin F. We have previously shown that mutations in CCNF cause disruptions to overall protein homeostasis; causing a build-up of ubiquitylated proteins (1) as well as defects in autophagic machinery (2). MethodsHere, we have used an unbiased proteomic screening workflow using BioID, as well as standard immunoprecipitations to identify novel interaction partners of cyclin F, identifying the interaction between cyclin F and a series of paraspeckle proteins. The homeostasis of these new cyclin F interaction partners, RBM14, NONO and SFPQ were monitored in primary neurons using immunoblotting. In addition, the homeostasis of RBM14 was compared between control and ALS/FTD patient tissue using standard IHC studies. ResultsUsing BioID, we found over 100 putative interaction partners of cyclin F and demonstrated that cyclin F closely associates with a number of essential paraspeckle proteins, which are stress-responsive proteins that have recently been implicated in ALS pathogenesis. We further demonstrate that the turnover of these novel binding partners are defective when cyclin F carries an ALS/FTD-causing mutation. In addition the analysis of RBM14 levels in ALS patient post-mortem tissue revealed that RBM14 levels were significantly reduced in post-mortem ALS patient motor cortex and significantly reduced in the neurons of spinal cord tissue. ConclusionOverall, our data demonstrate that the dysregulation of paraspeckle components may be contributing factors to the molecular pathogenesis of ALS/FTD. HighlightsO_LIPreviously, we identified missense mutations in CCNF that are linked to Amyotrophic lateral sclerosis/Frontotemporal dementia (ALS/FTD) and have shown that a single mutation in cyclin F can cause defects to major protein degradation systems in dividing cells. C_LIO_LICyclin F has very few known interaction partners, many of which have roles in cell cycle progression. Accordingly, we used BioID and mass spectrometry to identify novel binding partners of cyclin F that may reveal insight into the role of cyclin F in neurodegeneration. C_LIO_LIMass spectrometry and bioinformatic studies demonstrate that cyclin F interacts with several RNA binding proteins. This includes the essential paraspeckle proteins, RBM14. Notably, this interaction could be validated by standard immunoprecipitations and immunoblotting. Cyclin F could also be found to interact with a series of essential proteins which form the paraspeckle complex. C_LIO_LIWe further evaluated the effect of cyclin F(S621G) on the homeostasis of these novel interaction partners in primary neurons in response to a known paraspeckle inducer, MG132. Notably, we demonstrate significant defects in the homeostasis of RBM14 and SFPQ, but not NONO, when cyclin F carries an S621G mutation. C_LIO_LIUnlike other paraspeckle proteins, RBM14 levels have not previously been reported in the post-mortem brain and spinal cord of ALS patient post-mortem tissue. Here, we note significant defects in the homeostasis of RBM14 in the post-mortem tissue of ALS patients. C_LI

neuroscience↗

bioRxiv2019

Effects of Inaccurate Response Function Calibration on Characteristics of the Fiber Orientation Distribution in Diffusion MRI

Diffusion MRI of the brain enables to quantify white matter fiber orientations noninvasively. Several approaches have been proposed to estimate such characteristics from diffusion MRI data with spherical deconvolution being one of the most widely used methods. Constrained spherical deconvolution requires to define - or derive from the data - a response function, which is used to compute the fiber orientation distribution (FOD). This definition or derivation is not unequivocal and can thus result in different characteristics of the response function which are expected to affect the FOD computation and the subsequent fiber tracking. In this work, we explored the effects of inaccuracies in the shape and scaling factors of the response function on the FOD characteristics. With simulations, we show that underestimation of the shape factor in the response functions has a larger effect on the FOD peaks than overestimation of the shape factor, whereas the latter will cause more spurious peaks. Moreover, crossing fiber populations with a smaller separation angle were more sensitive to the response function inaccuracy than fiber populations with more orthogonal separation angles. Furthermore, the FOD characteristics show deviations as a result of modified shape and scaling factors of the response function. Results with the in vivo data demonstrate that the deviations of the FODs and spurious peaks can further deviate the termination of propagation in fiber tracking. This work highlights the importance of proper definition of the response function and how specific calibration factors can affect the FOD and fiber tractography results.

neuroscience↗

bioRxiv2019

Spherical deconvolution with tissue-specific response functions and multi-shell diffusion MRI to estimate multiple fiber orientation distributions (mFODs)

In diffusion MRI, spherical deconvolution approaches can estimate local white matter (WM) fiber orientation distributions (FOD) which can be used to produce fiber tractography reconstructions. The applicability of spherical deconvolution to grey matter (GM), however, is still limited, despite its critical role as start/endpoint of WM fiber pathways. The advent of multi-shell diffusion MRI data offers additional contrast to model the GM signal but, to date, only isotropic models have been applied to GM. Evidence from both histology and high-resolution diffusion MRI studies suggests a marked anisotropic character of the diffusion process in GM, which could be exploited to improve the description of the cortical organization. In this study, we investigated whether performing spherical deconvolution with tissue specific models of both WM and GM can improve the characterization of the latter while retaining state-of-the-art performances in WM. To this end, we developed a framework able to simultaneously accommodate multiple anisotropic response functions to estimate multiple, tissue-specific, fiber orientation distributions (mFODs). As proof of principle, we used the diffusion kurtosis imaging model to represent the WM signal, and the neurite orientation dispersion and density imaging (NODDI) model to represent the GM signal. The feasibility of the proposed approach is shown with numerical simulations and with data from the Human Connectome Project (HCP). The performance of our method is compared to the current state of the art, multi-shell constrained spherical deconvolution (MSCSD). The simulations show that with our new method we can accurately estimate a mixture of two FODs at SNR[≥]50. With HCP data, the proposed method was able to reconstruct both tangentially and radially oriented FODs in GM, and performed comparably well to MSCSD in computing FODs in WM. When performing fiber tractography, the trajectories reconstructed with mFODs reached the cortex with more spatial continuity and for a longer distance as compared to MSCSD and allowed to reconstruct short trajectories tangential to the cortical folding. In conclusion, we demonstrated that our proposed method allows to perform spherical deconvolution of multiple anisotropic response functions, specifically improving the performances of spherical deconvolution in GM tissue. HighlightsO_LIWe introduce a novel framework to perform spherical deconvolution with multiple anisotropic response functions (mFOD) C_LIO_LIWe show that the proposed framework can be used to improve the FOD estimation in the cortical grey matter C_LIO_LIFiber tractography performed with mFOD reaches the cortical GM with more coverage and contiguity than with previous methods C_LIO_LIThe proposed framework is a first step towards GM to GM fiber tractography C_LI

bioinformatics↗

bioRxiv2019

Automated characterization of noise distributions in diffusion MRI data

Knowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process. The use of parallel imaging methods, the number of receiver coils and imaging filters applied by the scanner, amongst other factors, dictate the resulting signal distribution. Accurate estimation beyond textbook Rician or noncentral chi distributions often requires information about the acquisition process (e.g., coils sensitivity maps or reconstruction coeffcients), which is usually not available. We introduce two new automated methods using the moments and maximum likelihood equations of the Gamma distribution to estimate noise distributions as they explicitly depend on the number of coils, making it possible to estimate all unknown parameters using only the magnitude data. A rejection step is used to make the framework automatic and robust to artifacts. Simulations using stationary and spatially varying noncentral chi noise distributions were created for two diffusion weightings with SENSE or GRAPPA reconstruction and 8, 12 or 32 receiver coils. Furthermore, MRI data of a water phantom with different combinations of parallel imaging were acquired on a 3T Philips scanner along with noise-only measurements. Finally, experiments on freely available datasets from a single subject acquired on a 3T GE scanner are used to assess reproducibility when limited information about the acquisition protocol is available. Additionally, we demonstrated the applicability of the proposed methods for a bias correction and denoising task on an in vivo dataset acquired on a 3T Siemens scanner. A generalized version of the bias correction framework for non integer degrees of freedom is also introduced. The proposed framework is compared with three other algorithms with datasets from three vendors, employing different reconstruction methods. Simulations showed that assuming a Rician distribution can lead to misestimation of the noise distribution in parallel imaging. Results on the acquired datasets showed that signal leakage in multiband can also lead to a misestimation of the noise distribution. Repeated acquisitions of in vivo datasets show that the estimated parameters are stable and have lower variability than compared methods. Results for the bias correction and denoising task show that the proposed methods reduce the appearance of noise at high b-value. The proposed algorithms herein can estimate both parameters of the noise distribution automatically, are robust to signal leakage artifacts and perform best when used on acquired noise maps.

neuroscience↗